Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	Louisiana	34,903
2	Florida	31,835
3	Mississippi	31,370
4	Alabama	29,564
5	Arizona	29,403
6	Georgia	27,228
7	South Carolina	26,746
8	Tennessee	26,448
9	Iowa	25,485
10	Arkansas	25,092
11	Texas	24,915
12	Nevada	24,646
13	North Dakota	23,568
14	New York	23,369
15	New Jersey	22,691
16	Rhode Island	22,296
17	Illinois	21,888
18	Nebraska	21,238
19	District of Columbia	21,190
20	Idaho	21,134
21	South Dakota	21,133
22	Delaware	20,093
23	Maryland	19,957
24	California	19,945
25	Utah	19,891
26	Oklahoma	19,410
27	Missouri	18,709
28	Massachusetts	18,504
29	North Carolina	18,495
30	Wisconsin	18,436
31	Kansas	18,336
32	Indiana	16,893
33	Virginia	16,461
34	Minnesota	15,970
35	Connecticut	15,574
36	Kentucky	14,641
37	New Mexico	13,152
38	Puerto Rico	13,100
39	Michigan	12,831
40	Ohio	12,345
41	Pennsylvania	12,101
42	Colorado	11,330
43	Washington	11,315
44	Alaska	10,531
45	Montana	9,660
46	Wyoming	8,416
47	Hawaii	8,146
48	West Virginia	7,841
49	Oregon	7,302
50	New Hampshire	5,844
51	Maine	3,778
52	Vermont	2,748

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	North Dakota	540
2	Wisconsin	384
3	South Dakota	380
4	Utah	323
5	Puerto Rico	310
6	Oklahoma	293
7	Arkansas	277
8	Iowa	268
9	Missouri	245
10	Tennessee	240
11	Alabama	217
12	Nebraska	200
13	Minnesota	194
14	Montana	188
15	Mississippi	160
16	Kansas	158
17	Idaho	157
18	Illinois	157
19	Georgia	153
20	Kentucky	149
21	South Carolina	146
22	Florida	144
23	Indiana	141
24	Texas	138
25	Louisiana	137
26	Alaska	132
27	Wyoming	126
28	Nevada	121
29	Virginia	119
30	North Carolina	116
31	West Virginia	116
32	Arizona	107
33	Colorado	94
34	California	93
35	Maryland	90
36	Delaware	84
37	District of Columbia	77
38	Ohio	77
39	Hawaii	76
40	Massachusetts	68
41	New Mexico	60
42	Oregon	58
43	Pennsylvania	54
44	New Jersey	52
45	Washington	46
46	Michigan	45
47	New York	45
48	Rhode Island	41
49	New Hampshire	32
50	Maine	29
51	Connecticut	13
52	Vermont	5

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,808
2	New York	1,680
3	Massachusetts	1,350
4	Connecticut	1,259
5	Louisiana	1,154
6	Rhode Island	1,027
7	Mississippi	944
8	District of Columbia	878
9	Arizona	752
10	Michigan	698
11	Illinois	686
12	Maryland	641
13	Delaware	637
14	Pennsylvania	628
15	South Carolina	621
16	Florida	619
17	Georgia	607
18	Texas	522
19	Indiana	520
20	Alabama	497
21	Nevada	497
22	New Mexico	404
23	Iowa	400
24	Ohio	394
25	Arkansas	391
26	California	380
27	Minnesota	357
28	Virginia	353
29	Colorado	351
30	New Hampshire	322
31	Tennessee	321
32	North Carolina	311
33	Missouri	307
34	Washington	279
35	North Dakota	255
36	Kentucky	254
37	Idaho	247
38	Oklahoma	239
39	Nebraska	236
40	South Dakota	228
41	Wisconsin	214
42	Kansas	204
43	Puerto Rico	190
44	West Virginia	172
45	Montana	146
46	Utah	138
47	Oregon	125
48	Maine	103
49	Vermont	92
50	Hawaii	84
51	Wyoming	84
52	Alaska	57

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Montana	4
2	North Dakota	4
3	Arizona	3
4	Florida	3
5	Georgia	3
6	Hawaii	3
7	Louisiana	3
8	Mississippi	3
9	South Dakota	3
10	Virginia	3
11	Alabama	2
12	Massachusetts	2
13	Nevada	2
14	New Mexico	2
15	Puerto Rico	2
16	South Carolina	2
17	Tennessee	2
18	Texas	2
19	West Virginia	2
20	Arkansas	1
21	California	1
22	Idaho	1
23	Illinois	1
24	Indiana	1
25	Kansas	1
26	Minnesota	1
27	Missouri	1
28	North Carolina	1
29	Oklahoma	1
30	Pennsylvania	1
31	Alaska	0
32	Colorado	0
33	Connecticut	0
34	Delaware	0
35	District of Columbia	0
36	Iowa	0
37	Kentucky	0
38	Maine	0
39	Maryland	0
40	Michigan	0
41	Nebraska	0
42	New Hampshire	0
43	New Jersey	0
44	New York	0
45	Ohio	0
46	Oregon	0
47	Rhode Island	0
48	Utah	0
49	Vermont	0
50	Washington	0
51	Wisconsin	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Chattahoochee	Georgia	148,895	1	99
Lincoln	Arkansas	148,188	2	99
Trousdale	Tennessee	146,313	3	99
Lafayette	Florida	146,046	4	99
Lake	Tennessee	124,430	5	99
Davidson	Tennessee	40,340	177	94
Richland	South Carolina	34,717	265	91
York	South Carolina	18,162	1084	65
Orange	California	16,809	1225	61
Pierce	Washington	9,305	2103	33

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	4,848	1	99
Galax city	Virginia	4,727	2	99
Emporia city	Virginia	4,676	3	99
Randolph	Georgia	4,131	4	99
Terrell	Georgia	3,634	5	99
Richland	South Carolina	541	711	77
Davidson	Tennessee	416	968	69
Orange	California	355	1117	64
York	South Carolina	235	1484	52
Pierce	Washington	233	1491	52

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.